A Fast Level Set Model for Intensity Inhomogeneity Correction in eHealth Analysis System

Wound area changes over multiple weeks are highly predictive of the wound healing process. Big data processing is considered as one of the main solutions for it. We usually analysis the images of wound bed. Unfortunately, accurate measurements of wound area changes are difficult. In this paper, we propose a novel level set model (LSM) for pre-processing the intensity inhomogeneity images before encoder. State-of-the-art LSMs can segment objects. However, most of these methods are time-consuming and inefficient. The proposed fast level set model (FLSM) is based on the piecewise constant and piecewise smooth Chan-Vese model and additive operator splitting algorithm. Different from conventional approaches, the proposed model integrates a new signed energy force function that can efficiently detect contours at weak or blurred edges. It ensures the smoothness of the level set function and reduces the computational complexity of re-initialization. Numerical synthetic and real world images demonstrate the advantages of the proposed method over state-of-the-art methods. Experimental results also show that proposed model is at least twice as fast as the widely used models.